Combination of Different Classifiers for Cardiac Arrhythmia Recognition
This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.
[1] U.R Achraya, Advances in Cardiac Signal Processing. New York:
Springer Publishing, 2007.
[2] A. Ghaffari , M. Atarod , M. R. Homaeinejad, Y. Ahmady and R.
Rahmani, "Detecting and Quantifying T-wave Alternans Using the
Correlation Method and Comparison with the FFT-based Method," 34th
Annual Conference of Computers in Cardiology (CinC), Bologna, Italy,
2008, pp.14-17.
[3] A. Ghaffari, M. R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Davaeeha, "Detecting and Discriminating Premature Atrial and
Ventricular Contractions: Application to Prediction of Paroxysmal Atrial
Fibrillation," 35th Annual Conference of Computers in Cardiology
(CinC), Lake City-Utah, USA, 2009, pp.13-16.
[4] A. Ghaffari, M. R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Davaeeha, "Detecting and Quantifying T-Wave Alternans in Patients
with Heart Failure and Non-Ischemic Cardiomyopathy via Modified
Spectral Method," 35th Annual Conference of Computers in Cardiology
(CinC), Lake City-Utah, USA, 2009, pp.13-16.
[5] A. Ghaffari, M. Atarod, M. R. Homaeinejad, and R. Rahmani, "On-Line
Identification of the Heart Hemodynamic Parameters via the Discrete-
Time Kalman-Bucy Filter Using Invasive Noisy Blood Pressure Waveform
Observations, " 34th Annual Conference of Computers in Cardiology
(CinC), Bologna, Italy, 2008, pp.14-17.
[6] A. Ghaffari, M. R. Homaeinejad, Y. Ahmadi and R. Rahmani, "An Open-
Source Computer Model for Visualization of Artificial Abnormal Multi-
Lead Electrocardiographic Phenomena," World Journal of Modelling and
Simulation, In-Press, 2009.
[7] M. G. Tsipouras and D. I. Fotiadis, "Automatic arrhythmia detection
based on time and time-frequency analysis of heart rate variability," Computer
Methods and Programs in Biomedicine, vol.74, pp. 95-108, 2004.
[8] I. Christov, G. Gomez-Herrero, V. Krasteva, I. Jekova, A. Gotchev and
K. Egiazarian, "Comparative study of morphological and time-frequency
ECG descriptors for heartbeat classification," Medical Engineering and
Physics, vol.28, pp. 876-887, 2006.
[9] Chia-Hung. Lin, "Frequency-domain features for ECG beat discrimination
using grey relational analysis-based classifier," Computers and
Mathematics with Applications, vol.55, pp. 680-690, 2008.
[10] Y. Ozbay, R. Ceylan and B. Karlik, "A fuzzy clustering neural network
architecture for classification of ECG arrhythmias," Computers in Biology
and Medicine, vol.36, pp. 376-388, 2006.
[11] Chia-Hung. Lin, Yi-Chun. Du and Tainsong. Chen, "Adaptive wavelet
network for multiple cardiac arrhythmias recognition," Expert Systems
with Applications, vol.34, pp. 2601-2611, 2008.
[12] P. de Chazal, M.O. Dwyer and R. B. Reilly , "Automatic Classification
of Heartbeats Using ECG Morphology and Heartbeat Interval
Features," IEEE Transactions on Biomed. Eng, vol.51, 2004.
[13] K. Nopone, J. Kortelainen and T. Seppanen, "Invariant trajectory
classification of dynamical systems with a case study on ECG," Pattern
Recognition, vol.42, pp.1832-1844, 2009.
[14] R.J. Povinelli, M.T. Johnson, A.C. Lindgren, F.M. Roberts and J. Ye,
"Statistical Models of Reconstructed Phase Spaces for Signal Classification,"
IEEE Transactions on Signal Processing, vol.54, 2006.
[15] M.I. Owis, A.H. Abou-Zied, A.M. Youssef and Y. M. Kadah, "Study of
Features Based on Nonlinear Dynamical Modeling in ECG Arrhythmia
Detection and Classification," IEEE Transactions on Biomed. Eng, vol.49,
2002.
[16] S.N. Yu and Y.H. Chen, "Noise-tolerant electrocardiogram beat classification
based on higher order statistics of subband components," Artificial
Intelligence in Medicine , vol.46, pp.165-178, 2009.
[17] L. Khadra, A.S. Al-Fahoum and S. Binajjaj, "A Quantitative Analysis
Approach for Cardiac Arrhythmia Classification Using Higher Order
Spectral Techniques," IEEE Transactions on Biomed. Eng, vol.52, 2005.
[18] S. Kara, M. Okandan, "Atrial fibrillation classification with artificial
neural networks," Pattern Recognition, vol.40, pp.2967-2973, 2007.
[19] M. Stridh, L. Srnmo, C.J. Meurling and S.B. Olsson, "Sequential Characterization
of Atrial Tachyarrhythmias Based on ECG Time-Frequency
Analysis," IEEE Transactions on Biomed. Eng, vol.51, 2004.
[20] D. Benitez, P.A. Gaydecki, A. Zaidi and A.P. Fitzpatrick, "The use
of the Hilbert transform in ECG signal analysis," Computers in Biology
and Medicine, vol.31, pp.399-406, 2001.
[21] F.A. Minhas and M. Arif, "Robust electrocardiogram (ECG) beat classification
using discrete wavelet transform," Physiological Measurement,
vol.29, pp.555-570, 2008.
[22] H. Liu, J. Sun, L. Liu and H. Zhang, "Feature selection with dynamic
mutual information," Pattern Recognition , vol.42, pp.1330-1339, 2009.
[23] H. Peng, F. Long and C. Ding, "Feature Selection Based on Mutual
Information: Criteria of Max-Dependency, Max-Relevance, and Min-
Redundancy," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol.27, 2005.
[24] S.N. Yu and Y.H. Chen, "Noise-tolerant electrocardiogram beat classification
based on higher order statistics of subband components," Artificial
Intelligence in Medicine, vol.46, pp.165-178, 2005.
[25] S.N. Yu and Kuan-To. Chou, "Integration of independent component
analysis and neural networks for ECG beat classification," Expert Systems
with Applications, vol.34, pp.2841-2846, 2008.
[26] S. Osowski, T. Markiewicz, L. Tran Hoai, "Recognition and classification
system of arrhythmia using ensemble of neural networks," Measurement,
vol.41, pp.610-617, 2008.
[27] H. Gholam Hosseini, D. Luo and K.J. Reynolds, "The comparison
of different feed forward neural network architectures for ECG signal
diagnosis," Medical Engineering and Physics, vol.28, pp.372-378, 2006.
[28] S. Kara, M. Okandan, "Atrial fibrillation classification with artificial
neural networks," Pattern Recognition, vol.40, pp.2967-2973, 2007.
[29] F. Melgani and Y. Bazi, "Classification of Electrocardiogram Signals
With Support Vector Machines and Particle Swarm Optimization," IEEE
Transactions on Information Technology in Biomedicine, vol.12, pp.667-
677, 2008.
[30] Chia-Hung. Lin, Yi-Chun. Du and Tainsong. Chen, "Adaptive wavelet
network for multiple cardiac arrhythmias recognition," Expert Systems
with Applications, vol.34, pp.2601-2611, 2008.
[31] I. Christov, G. Gomez-Herrero, V. Krasteva, I. Jekova, A. Gotchev and
K. Egiazarian, "Comparative study of morphological and time-frequency
ECG descriptors for heartbeat classification," Medical Engineering and
Physics , vol.28, pp.876-887, 2006.
[32] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, "An arrhythmia classification
system based on the RR-interval signal," Artificial Intelligence in
Medicine, vol.33, pp.237-250, 2005.
[33] R. Ceylan, Y. Uzbay and B. Karlik, "A novel approach for classification
of ECG arrhythmias: Type-2 fuzzy clustering neural network," Expert
Systems with Applications, vol.36, pp.6721-6726, 2009.
[34] K. Polat, S. Sahan and S. Gune, "A new method to medical diagnosis:
Artificial immune recognition system (AIRS) with fuzzy weighted preprocessing
and application to ECG arrhythmia," Expert Systems with
Applications, vol.31, pp.264-269, 2006.
[35] T.P. Exarchos, M.G. Tsipouras, C.P. Exarchos, C. Papaloukas, D.I. Fotiadis
and L.K. Michalis, "A methodology for the automated creation
of fuzzy expert systems for ischaemic and arrhythmic beat classification
based on a set of rules obtained by a decision tree," Artificial Intelligence
in Medicine, vol.40, pp.187-200, 2007.
[36] N. Kannathal, C.M. Lim, U. Rajendra Acharya and P.K. Sadasivan,
"Cardiac state diagnosis using adaptive neuro-fuzzy technique," Medical
Engineering and Physics, vol.28, pp.809-815, 2006.
[37] L. Tran Hoai, S. Osowski and M. Stodolski, "On-Line Heart beat
recognition using Hermite polynomials and neuro-fuzzy network," IEEE
Transactions on Biomedical Engineering, vol.52, pp.1224-1231, 2003.
[38] A. Ghaffari, M.R. Homaeinezhad, M. Khazraee and M. Daevaeiha,
"Segmentation of Holter ECG Waves via Analysis of a Discrete Wavelet-
Derived Multiple Skewness-Kurtosis Based Metric," Annals of Biomedical
Engineering, Springer Publishing, vol.38, pp.1497-1510, 2010.
[39] A. Ghaffari, M.R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Daevaeiha, "A Robust Wavelet-based Multi-Lead Electrocardiogram
Delineation Algorithm," Medical Engineering and Physics, vol.31,
pp.1219-1227, 2009.
[40] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, 1999.
[41] C.M. Bishop, Pattern Recognition and Machine Learning. New York:
Springer Publishing, 2006.
[42] D. Benitez, P.A. Gaydecki, A. Zaidi and A.P. Fitzpatrick, "The use
of the Hilbert transform in ECG signal analysis," Computers in Biology
and Medicine, vol.31, pp.399-406, 2001.
[43] A. Ghaffari, M.R. Homaeinezhad, M. Atarod, and M. Akraminia,
"Parallel Processing of ECG and Blood Pressure Waveforms for Detection
of Acute Hypotensive Episodes: A Simulation Study Using a Risk Scoring
Model," Computer Methods in Biomechanics and Biomedical Engineeing,
Taylor and Francis Publishing, In-Press, 2009.
[44] M.R. Homaeinezhad, H. Najjaran Toosi, A. Ghaffari, M. Tahmasebi
and M.M. Daevaeiha, "Long-Duration Ambulatory Holter ECG QRS
Complex Geometrical Templates Extraction by Non-Parametric Clustering
of the QRS Virtual Close-up Extracted Feature Space," 36th
Annual Conference of Computers in Cardiology (CinC), Belfast, UK,
2010, pp.24-27.
[45] Mrs.B. Anuradha and V.C. Veera Reddy, "cardiac arrhythmia classification
using fuzzy classifiers," Journal of Theoretical and Applied
Information Technology, vol.4, pp.353-359, 2008.
[46] O.T. Inan, L. Giovangrandi and G.T.A. Kovacs, "Robust neuralnetwork-
based classification of premature ventricular contractions using
wavelet transform and timing interval features," IEEE Transactions on
Biomedical Engineering, vol.53, pp.2507-2515, 2006.
[1] U.R Achraya, Advances in Cardiac Signal Processing. New York:
Springer Publishing, 2007.
[2] A. Ghaffari , M. Atarod , M. R. Homaeinejad, Y. Ahmady and R.
Rahmani, "Detecting and Quantifying T-wave Alternans Using the
Correlation Method and Comparison with the FFT-based Method," 34th
Annual Conference of Computers in Cardiology (CinC), Bologna, Italy,
2008, pp.14-17.
[3] A. Ghaffari, M. R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Davaeeha, "Detecting and Discriminating Premature Atrial and
Ventricular Contractions: Application to Prediction of Paroxysmal Atrial
Fibrillation," 35th Annual Conference of Computers in Cardiology
(CinC), Lake City-Utah, USA, 2009, pp.13-16.
[4] A. Ghaffari, M. R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Davaeeha, "Detecting and Quantifying T-Wave Alternans in Patients
with Heart Failure and Non-Ischemic Cardiomyopathy via Modified
Spectral Method," 35th Annual Conference of Computers in Cardiology
(CinC), Lake City-Utah, USA, 2009, pp.13-16.
[5] A. Ghaffari, M. Atarod, M. R. Homaeinejad, and R. Rahmani, "On-Line
Identification of the Heart Hemodynamic Parameters via the Discrete-
Time Kalman-Bucy Filter Using Invasive Noisy Blood Pressure Waveform
Observations, " 34th Annual Conference of Computers in Cardiology
(CinC), Bologna, Italy, 2008, pp.14-17.
[6] A. Ghaffari, M. R. Homaeinejad, Y. Ahmadi and R. Rahmani, "An Open-
Source Computer Model for Visualization of Artificial Abnormal Multi-
Lead Electrocardiographic Phenomena," World Journal of Modelling and
Simulation, In-Press, 2009.
[7] M. G. Tsipouras and D. I. Fotiadis, "Automatic arrhythmia detection
based on time and time-frequency analysis of heart rate variability," Computer
Methods and Programs in Biomedicine, vol.74, pp. 95-108, 2004.
[8] I. Christov, G. Gomez-Herrero, V. Krasteva, I. Jekova, A. Gotchev and
K. Egiazarian, "Comparative study of morphological and time-frequency
ECG descriptors for heartbeat classification," Medical Engineering and
Physics, vol.28, pp. 876-887, 2006.
[9] Chia-Hung. Lin, "Frequency-domain features for ECG beat discrimination
using grey relational analysis-based classifier," Computers and
Mathematics with Applications, vol.55, pp. 680-690, 2008.
[10] Y. Ozbay, R. Ceylan and B. Karlik, "A fuzzy clustering neural network
architecture for classification of ECG arrhythmias," Computers in Biology
and Medicine, vol.36, pp. 376-388, 2006.
[11] Chia-Hung. Lin, Yi-Chun. Du and Tainsong. Chen, "Adaptive wavelet
network for multiple cardiac arrhythmias recognition," Expert Systems
with Applications, vol.34, pp. 2601-2611, 2008.
[12] P. de Chazal, M.O. Dwyer and R. B. Reilly , "Automatic Classification
of Heartbeats Using ECG Morphology and Heartbeat Interval
Features," IEEE Transactions on Biomed. Eng, vol.51, 2004.
[13] K. Nopone, J. Kortelainen and T. Seppanen, "Invariant trajectory
classification of dynamical systems with a case study on ECG," Pattern
Recognition, vol.42, pp.1832-1844, 2009.
[14] R.J. Povinelli, M.T. Johnson, A.C. Lindgren, F.M. Roberts and J. Ye,
"Statistical Models of Reconstructed Phase Spaces for Signal Classification,"
IEEE Transactions on Signal Processing, vol.54, 2006.
[15] M.I. Owis, A.H. Abou-Zied, A.M. Youssef and Y. M. Kadah, "Study of
Features Based on Nonlinear Dynamical Modeling in ECG Arrhythmia
Detection and Classification," IEEE Transactions on Biomed. Eng, vol.49,
2002.
[16] S.N. Yu and Y.H. Chen, "Noise-tolerant electrocardiogram beat classification
based on higher order statistics of subband components," Artificial
Intelligence in Medicine , vol.46, pp.165-178, 2009.
[17] L. Khadra, A.S. Al-Fahoum and S. Binajjaj, "A Quantitative Analysis
Approach for Cardiac Arrhythmia Classification Using Higher Order
Spectral Techniques," IEEE Transactions on Biomed. Eng, vol.52, 2005.
[18] S. Kara, M. Okandan, "Atrial fibrillation classification with artificial
neural networks," Pattern Recognition, vol.40, pp.2967-2973, 2007.
[19] M. Stridh, L. Srnmo, C.J. Meurling and S.B. Olsson, "Sequential Characterization
of Atrial Tachyarrhythmias Based on ECG Time-Frequency
Analysis," IEEE Transactions on Biomed. Eng, vol.51, 2004.
[20] D. Benitez, P.A. Gaydecki, A. Zaidi and A.P. Fitzpatrick, "The use
of the Hilbert transform in ECG signal analysis," Computers in Biology
and Medicine, vol.31, pp.399-406, 2001.
[21] F.A. Minhas and M. Arif, "Robust electrocardiogram (ECG) beat classification
using discrete wavelet transform," Physiological Measurement,
vol.29, pp.555-570, 2008.
[22] H. Liu, J. Sun, L. Liu and H. Zhang, "Feature selection with dynamic
mutual information," Pattern Recognition , vol.42, pp.1330-1339, 2009.
[23] H. Peng, F. Long and C. Ding, "Feature Selection Based on Mutual
Information: Criteria of Max-Dependency, Max-Relevance, and Min-
Redundancy," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol.27, 2005.
[24] S.N. Yu and Y.H. Chen, "Noise-tolerant electrocardiogram beat classification
based on higher order statistics of subband components," Artificial
Intelligence in Medicine, vol.46, pp.165-178, 2005.
[25] S.N. Yu and Kuan-To. Chou, "Integration of independent component
analysis and neural networks for ECG beat classification," Expert Systems
with Applications, vol.34, pp.2841-2846, 2008.
[26] S. Osowski, T. Markiewicz, L. Tran Hoai, "Recognition and classification
system of arrhythmia using ensemble of neural networks," Measurement,
vol.41, pp.610-617, 2008.
[27] H. Gholam Hosseini, D. Luo and K.J. Reynolds, "The comparison
of different feed forward neural network architectures for ECG signal
diagnosis," Medical Engineering and Physics, vol.28, pp.372-378, 2006.
[28] S. Kara, M. Okandan, "Atrial fibrillation classification with artificial
neural networks," Pattern Recognition, vol.40, pp.2967-2973, 2007.
[29] F. Melgani and Y. Bazi, "Classification of Electrocardiogram Signals
With Support Vector Machines and Particle Swarm Optimization," IEEE
Transactions on Information Technology in Biomedicine, vol.12, pp.667-
677, 2008.
[30] Chia-Hung. Lin, Yi-Chun. Du and Tainsong. Chen, "Adaptive wavelet
network for multiple cardiac arrhythmias recognition," Expert Systems
with Applications, vol.34, pp.2601-2611, 2008.
[31] I. Christov, G. Gomez-Herrero, V. Krasteva, I. Jekova, A. Gotchev and
K. Egiazarian, "Comparative study of morphological and time-frequency
ECG descriptors for heartbeat classification," Medical Engineering and
Physics , vol.28, pp.876-887, 2006.
[32] M.G. Tsipouras, D.I. Fotiadis and D. Sideris, "An arrhythmia classification
system based on the RR-interval signal," Artificial Intelligence in
Medicine, vol.33, pp.237-250, 2005.
[33] R. Ceylan, Y. Uzbay and B. Karlik, "A novel approach for classification
of ECG arrhythmias: Type-2 fuzzy clustering neural network," Expert
Systems with Applications, vol.36, pp.6721-6726, 2009.
[34] K. Polat, S. Sahan and S. Gune, "A new method to medical diagnosis:
Artificial immune recognition system (AIRS) with fuzzy weighted preprocessing
and application to ECG arrhythmia," Expert Systems with
Applications, vol.31, pp.264-269, 2006.
[35] T.P. Exarchos, M.G. Tsipouras, C.P. Exarchos, C. Papaloukas, D.I. Fotiadis
and L.K. Michalis, "A methodology for the automated creation
of fuzzy expert systems for ischaemic and arrhythmic beat classification
based on a set of rules obtained by a decision tree," Artificial Intelligence
in Medicine, vol.40, pp.187-200, 2007.
[36] N. Kannathal, C.M. Lim, U. Rajendra Acharya and P.K. Sadasivan,
"Cardiac state diagnosis using adaptive neuro-fuzzy technique," Medical
Engineering and Physics, vol.28, pp.809-815, 2006.
[37] L. Tran Hoai, S. Osowski and M. Stodolski, "On-Line Heart beat
recognition using Hermite polynomials and neuro-fuzzy network," IEEE
Transactions on Biomedical Engineering, vol.52, pp.1224-1231, 2003.
[38] A. Ghaffari, M.R. Homaeinezhad, M. Khazraee and M. Daevaeiha,
"Segmentation of Holter ECG Waves via Analysis of a Discrete Wavelet-
Derived Multiple Skewness-Kurtosis Based Metric," Annals of Biomedical
Engineering, Springer Publishing, vol.38, pp.1497-1510, 2010.
[39] A. Ghaffari, M.R. Homaeinezhad, M. Akraminia, M. Atarod, and
M. Daevaeiha, "A Robust Wavelet-based Multi-Lead Electrocardiogram
Delineation Algorithm," Medical Engineering and Physics, vol.31,
pp.1219-1227, 2009.
[40] S. Mallat, A Wavelet Tour of Signal Processing. Academic Press, 1999.
[41] C.M. Bishop, Pattern Recognition and Machine Learning. New York:
Springer Publishing, 2006.
[42] D. Benitez, P.A. Gaydecki, A. Zaidi and A.P. Fitzpatrick, "The use
of the Hilbert transform in ECG signal analysis," Computers in Biology
and Medicine, vol.31, pp.399-406, 2001.
[43] A. Ghaffari, M.R. Homaeinezhad, M. Atarod, and M. Akraminia,
"Parallel Processing of ECG and Blood Pressure Waveforms for Detection
of Acute Hypotensive Episodes: A Simulation Study Using a Risk Scoring
Model," Computer Methods in Biomechanics and Biomedical Engineeing,
Taylor and Francis Publishing, In-Press, 2009.
[44] M.R. Homaeinezhad, H. Najjaran Toosi, A. Ghaffari, M. Tahmasebi
and M.M. Daevaeiha, "Long-Duration Ambulatory Holter ECG QRS
Complex Geometrical Templates Extraction by Non-Parametric Clustering
of the QRS Virtual Close-up Extracted Feature Space," 36th
Annual Conference of Computers in Cardiology (CinC), Belfast, UK,
2010, pp.24-27.
[45] Mrs.B. Anuradha and V.C. Veera Reddy, "cardiac arrhythmia classification
using fuzzy classifiers," Journal of Theoretical and Applied
Information Technology, vol.4, pp.353-359, 2008.
[46] O.T. Inan, L. Giovangrandi and G.T.A. Kovacs, "Robust neuralnetwork-
based classification of premature ventricular contractions using
wavelet transform and timing interval features," IEEE Transactions on
Biomedical Engineering, vol.53, pp.2507-2515, 2006.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52732", author = "M. R. Homaeinezhad and E. Tavakkoli and M. Habibi and S. A. Atyabi and A. Ghaffari", title = "Combination of Different Classifiers for Cardiac Arrhythmia Recognition", abstract = "This paper describes a new supervised fusion (hybrid)
electrocardiogram (ECG) classification solution consisting of a new
QRS complex geometrical feature extraction as well as a new version
of the learning vector quantization (LVQ) classification algorithm
aimed for overcoming the stability-plasticity dilemma. Toward this
objective, after detection and delineation of the major events of ECG
signal via an appropriate algorithm, each QRS region and also its
corresponding discrete wavelet transform (DWT) are supposed as
virtual images and each of them is divided into eight polar sectors.
Then, the curve length of each excerpted segment is calculated
and is used as the element of the feature space. To increase the
robustness of the proposed classification algorithm versus noise,
artifacts and arrhythmic outliers, a fusion structure consisting of
five different classifiers namely as Support Vector Machine (SVM),
Modified Learning Vector Quantization (MLVQ) and three Multi
Layer Perceptron-Back Propagation (MLP–BP) neural networks with
different topologies were designed and implemented. The new proposed
algorithm was applied to all 48 MIT–BIH Arrhythmia Database
records (within–record analysis) and the discrimination power of the
classifier in isolation of different beat types of each record was
assessed and as the result, the average accuracy value Acc=98.51%
was obtained. Also, the proposed method was applied to 6 number
of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging
to 20 different records of the aforementioned database (between–
record analysis) and the average value of Acc=95.6% was achieved.
To evaluate performance quality of the new proposed hybrid learning
machine, the obtained results were compared with similar peer–
reviewed studies in this area.", keywords = "Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron,Fusion (Hybrid) Classification, Arrhythmia Classification,Supervised Learning Machine.", volume = "5", number = "3", pages = "581-12", }
